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دسته بندی:
داده کاوی - data mining
سال انتشار:
2018
عنوان انگلیسی مقاله:
Mining data in a dynamic PRA framework
ترجمه فارسی عنوان مقاله:
داده های معدن در یک چارچوب PRA پویا
منبع:
Sciencedirect - Elsevier - Progress in Nuclear Energy, 108 (2018) 99-110. doi:10.1016/j.pnucene.2018.05.004
نویسنده:
D. Mandelli∗, D. Maljovec, A. Alfonsi, C. Parisi, P. Talbot, J. Cogliati, C. Smith, C. Rabiti
چکیده انگلیسی:
Computational, also known as Dynamic, Probabilistic Risk Assessment (PRA) methods employ system simulation
codes coupled with stochastic analysis tools in order to determine probabilities of certain outcomes such as
system failure. In contrast to Classical PRA methods (i.e., Event-Tree and Fault-Tree) in which timing and se
quencing of events is set by the analyst, accident progression is dictated by the system control logic and its
interaction with the system temporal evolution. Due to the nature of the problem, Dynamic PRA methods can be
expensive form a computational point of view since a large number of accident scenarios is simulated.
Consequently, they also generate a large amount of data (database storage may be on the order of gigabytes or
higher). We investigate and apply several methods and algorithms to analyze these large time-dependent data
sets. The objective is to present a broad overview of methods and algorithms that can be used to improve data
quality and to analyze and extract information from large data sets containing time dependent data. In this
context, “extracting information” means constructing input-output correlations, finding commonalities, and
identifying outliers.
Keywords: Data mining ، Dynamic PRA ، Probabilistic risk assessment ، Clustering
قیمت: رایگان
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